A fast algorithm for sparse multichannel blind deconvolution

Nose-Filho, Kenji; Takahata, André K.; Lopes, Renato da Rocha; Romano, João Marcos Travassos

doi:10.1190/geo2015-0069.1

Cited by 34 publications

(18 citation statements)

References 35 publications

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“…For this test, 20 traces are generated with a sampling frequency of 500 Hz using the reflectivity shown in Figure 1b, which can be downloaded from [29]. The received data 1 In equation (16) of [17] there is a typo for the k-th component of ∇L, which should be in Figure 1c is the convolution of the reflectivity with a Ricker wavelet of center frequency 40 Hz with 50 degrees of phase shift (see Figure 1a) plus additive white Gaussian noise (AWGN) of SNR = 10 dB. The SNR adopted in this work for the signal-plus-noise model, i.e., x = s + n is defined as SNR = 10 log 10 s 2…”

Section: Synthetic Data Testmentioning

confidence: 99%

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Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

et al. 2019

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In this work, an efficient numerical scheme is presented for seismic blind deconvolution in a multichannel scenario. The proposed method iterate with two steps: first, wavelet estimation across all channels and second, refinement of the reflectivity estimate simultaneously in all channels using sparse deconvolution. The reflectivity update step is formulated as a basis pursuit denoising problem and a sparse solution is obtained with the spectral projected-gradient algorithm -faithfulness to the recorded traces is constrained by the measured noise level. Wavelet re-estimation has a closed form solution when performed in the frequency domain by finding the minimum energy wavelet common to all channels. Nothing is assumed known about the wavelet apart from its time duration. In tests with both synthetic and real data, the method yields sparse reflectivity series and stable wavelet estimates results compared to existing methods with significantly less computational effort.

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Section: Synthetic Data Testmentioning

confidence: 99%

“…In seismic applications, conventional multichannel methods cannot be applied directly. The major cause is the great similarity between neighboring reflectivity sequences, which makes the problem either numerically sensitive or, at worst, ill-posed and impossible to solve [17].…”

Section: Introductionmentioning

confidence: 99%

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

et al. 2019

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“…Severe non-uniqueness issues are inherent to BD; there could be many possible g i and s pairs whose convolution will result in the observed data. In order to alleviate these non-uniqueness issues, recent BD algorithms in geophysics: 1. take advantage of the multichannel nature of the seismic data (Kaaresen and Taxt, 1998;Kazemi and Sacchi, 2014;Nose-Filho et al, 2015;Liu et al, 2016); 2. sensibly choose the initial estimates of the g i in order to converge to a desired solution (Liu et al, 2016); and/or 3. constrain the sparsity of the g i (Kazemi and Sacchi, 2014). Kazemi et al (2016) used sparse multichannel BD to estimate source and receiver wavelets while processing land seismic data.…”

Section: Introductionmentioning

confidence: 99%

Focused blind deconvolution of interferometric Green’s functions

Bharadwaj

Demanet

Fournier

2018

SEG Technical Program Expanded Abstracts 2018

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We detail a novel multichannel blind deconvolution (BD) algorithm that extracts the cross-correlated or interferometric Green's functions from the records due to a single noisy source. In this framework, we perform a least-squares fit of the crosscorrelated records, rather than the raw records, which greatly reduces the indeterminacy inherent to traditional BD methods. To resolve the remaining degrees of freedom, we seek a first approximation where the Green's functions are "maximally white", and relax this requirement as the iterations progress. This requirement is encoded as the focusing near zero lag of the energy of the auto-correlated Green's functions, hence we call the method focused blind deconvolution (FBD). We demonstrate the benefits of FBD using synthetic seismic-while-drilling experiments to look around and ahead of a borehole. Here, the noise due to the operation of the drill bit is not directly usable for reflection imaging, but FBD can provide the processing needed to extract the noise signature without unrealistically assuming the drill noise to be uncorrelated. The interferometric Green's functions obtained from FBD can either be directly imaged or further processed to output the usual subsurface Green's functions. Note that FBD is designed for an acquisition where the noise is recorded for a longer time period than the propagation time of the seismic waves e.g., as could be done during normal drilling operations. Traditional seismic imaging may now be augmented by added information around and ahead of the drill bit, potentially allowing less frequent traditional surveys. * Surveys of BD algorithms in the signal and image processing literature are given in Kundur and Hatzinakos (1996) and Campisi and Egiazarian (2016).

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“…Neste contexto, os algoritmos de busca baseados no gradiente, comumente utilizados para a obtenção dos coeficientes de um filtro de desconvolução [7], são bastante dependentes da inicialização, normalmente ficando presos em soluções subotimas. Para estas situações, o uso de algoritmos evolutivos torna-se uma opção bastante atraente.…”

Section: Introductionunclassified

Estratégias Evolutivas para o Problema de Desconvolução Sı́smica

Silva¹,

Oliveira²,

Nose-Filho³

et al. 2017

Anais De XXXV Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-O problema de desconvolução sísmica, baseado na hipótese de esparsidade do perfil de refletividade, requer a otimização de uma função custo não-linear e multimodal. Neste trabalho, investigamos o uso de diferentes algoritmos evolutivos para realizar a busca dos parâmetros do filtro de desconvolução que minimizam a função pseudo-Huber normalizada, os quais possuem mecanismos para escapar deótimos locais e explorar de forma mais ampla o espaço de soluções candidatas. Os resultados obtidos com dados sintéticos mostram o progresso de desempenho dos filtros de desconvolução projetados com estes algoritmos, quando comparados a um método do tipo gradiente estocástico. Palavras-Chave-Desconvolução sísmica, Desconvolução esparsa, Algoritmos evolutivos.

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A fast algorithm for sparse multichannel blind deconvolution

Cited by 34 publications

References 35 publications

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

Sparse Multichannel Blind Deconvolution of Seismic Data via Spectral Projected-Gradient

Focused blind deconvolution of interferometric Green’s functions

Estratégias Evolutivas para o Problema de Desconvolução Sı́smica

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